It is a wide-spread way to detect malware by analyzing its behavioral characteristics based
on API call sequences. However, previous studies usually just focus on its static or dynamic …

Malware API call graph derived from API call sequences is considered as a representative
technique to understand the malware behavioral characteristics. However, it is troublesome …

The sharing of malicious code libraries and techniques over the Internet has vastly
increased the release of new malware variants in an unprecedented rate. Malware variants …

Dynamic malware detection executes the software in a secured virtual environment and
monitors its run-time behavior. This technique widely uses API sequence analysis to identify …

To handle the security threat faced by the widespread use of Internet of Things (IoT) devices
due to the ever-lasting increase of malware, the security researchers increasingly rely on …

In recent years, malware has emerged as a critical security threat. In addition, malware
authors continue to embed numerous anti-detection features to evade the existing malware …

In this paper, a new deep learning and machine learning combined model is proposed for
malware behavior analysis. One part of it analyzes the dependency relation in API …

Abstract Application Programming Interfaces (APIs) are widely considered a useful data
source for dynamic malware analysis to understand the behavioral characteristics of …

The widespread development of the malware industry is considered the main threat to our e-
society. Therefore, malware analysis should also be enriched with smart heuristic tools that …

Malware classification plays an important role in tracing the attack sources of computer
security. However, existing static analysis methods are fast in classification, but they are …